class_docstr: Computing filter banks is applying triangular filters on a Mel-scale to the power
   spectrum to extract frequency bands. Return a float tensor with shape
   (num_channels, num_frames, num_frequencies).
docstr: Set params.
  :param config: contains thirteen optional parameters:
         --window_length                              : Window length in seconds. (float, default = 0.025)
         --frame_length                               : Hop length in seconds. (float, default = 0.010)
         --snip_edges                         : If true, the last frame (shorter than window_length) will be
                                       cutoff. If ,false 1 // 2 frame_length data will be padded
                                       to data. (bool, default = true)
         ---raw_energy                                : If 1, compute frame energy before preemphasis and
                                       windowing. If 2,  compute frame energy after
                                       preemphasis and windowing. (int, default = 1)
         --preeph_coeff                               : Coefficient for use in frame-signal preemphasis.
                                      (float, default = 0.97)
         --window_type                                : Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
                                      (string, default = "povey")
         --remove_dc_offset                   : Subtract mean from waveform on each frame.
                                       (bool, default = true)
         --is_fbank                                   : If true, compute power spetrum without frame energy.
                                       If false, using the frame energy instead of the
                                       square of the constant component of the signal.
                                       (bool, default = true)
         --is_log10                  : If true, using log10 to fbank. If false, using loge.
                                            (bool, default = false)
         --output_type                                : If 1, return power spectrum. If 2, return log-power
                                       spectrum. (int, default = 1)
         --upper_frequency_limit              : High cutoff frequency for mel bins (if <= 0, offset
                                      from Nyquist) (float, default = 0)
         --lower_frequency_limit              : Low cutoff frequency for mel bins (float, default = 20)
         --filterbank_channel_count   : Number of triangular mel-frequency bins.
                                      (float, default = 23)
         --dither                             : Dithering constant (0.0 means no dither).
                                      (float, default = 1) [add robust to training]
  :return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
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code: def params(cls, config=None):
      """
      Set params.
      :param config: contains thirteen optional parameters:
             --window_length				: Window length in seconds. (float, default = 0.025)
             --frame_length				: Hop length in seconds. (float, default = 0.010)
             --snip_edges				: If true, the last frame (shorter than window_length) will be
                                           cutoff. If ,false 1 // 2 frame_length data will be padded
                                           to data. (bool, default = true)
             ---raw_energy				: If 1, compute frame energy before preemphasis and
                                           windowing. If 2,  compute frame energy after
                                           preemphasis and windowing. (int, default = 1)
             --preeph_coeff				: Coefficient for use in frame-signal preemphasis.
                                          (float, default = 0.97)
             --window_type				: Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
                                          (string, default = "povey")
             --remove_dc_offset			: Subtract mean from waveform on each frame.
                                           (bool, default = true)
             --is_fbank					: If true, compute power spetrum without frame energy.
                                           If false, using the frame energy instead of the
                                           square of the constant component of the signal.
                                           (bool, default = true)
             --is_log10                  : If true, using log10 to fbank. If false, using loge.
                                                (bool, default = false)
             --output_type				: If 1, return power spectrum. If 2, return log-power
                                           spectrum. (int, default = 1)
             --upper_frequency_limit		: High cutoff frequency for mel bins (if <= 0, offset
                                          from Nyquist) (float, default = 0)
             --lower_frequency_limit		: Low cutoff frequency for mel bins (float, default = 20)
             --filterbank_channel_count	: Number of triangular mel-frequency bins.
                                          (float, default = 23)
             --dither			    	: Dithering constant (0.0 means no dither).
                                          (float, default = 1) [add robust to training]
      :return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
      """
  
      upper_frequency_limit = 0.0
      lower_frequency_limit = 20.0
      filterbank_channel_count = 23.0
      window_length = 0.025
      frame_length = 0.010
      output_type = 1
      sample_rate = 16000
      snip_edges = True
      raw_energy = 1
      preeph_coeff = 0.97
      window_type = "povey"
      remove_dc_offset = True
      is_fbank = True
      dither = 0.0
      is_log10 = False
  
      hparams = HParams(cls=cls)
      hparams.add_hparam("upper_frequency_limit", upper_frequency_limit)
      hparams.add_hparam("lower_frequency_limit", lower_frequency_limit)
      hparams.add_hparam(
          "filterbank_channel_count", filterbank_channel_count
      )
      hparams.add_hparam("window_length", window_length)
      hparams.add_hparam("frame_length", frame_length)
      hparams.add_hparam("output_type", output_type)
      hparams.add_hparam("sample_rate", sample_rate)
      hparams.add_hparam("snip_edges", snip_edges)
      hparams.add_hparam("raw_energy", raw_energy)
      hparams.add_hparam("preeph_coeff", preeph_coeff)
      hparams.add_hparam("window_type", window_type)
      hparams.add_hparam("remove_dc_offset", remove_dc_offset)
      hparams.add_hparam("is_fbank", is_fbank)
      hparams.add_hparam("dither", dither)
      hparams.add_hparam("is_log10", is_log10)
  
      if config is not None:
          hparams.override_from_dict(config)
  
      return hparams
  
file_path: delta/delta/data/frontend/fbank.py
imports: import tensorflow
  from core.ops import py_x_ops
  from delta.utils.hparam import HParams
  from delta.data.frontend.base_frontend import BaseFrontend
  from delta.data.frontend.spectrum import Spectrum
label_desc: Use image node_id 2 for calling the Fbank obj's underlying member method code with example usage: obj.params(cls, config) and returns: hparams